Volume 13 | Issue 2 |

 Abstract

False advertising, involving the dissemination of deceptive or misleading claims, poses significant risks to consumer welfare, market integrity, and public trust. This research critically examines the criminal aspects of false advertising, differentiating them from civil remedies, and explores the evolution of regulatory frameworks aimed at protecting consumers. Drawing on legal statutes, judicial precedents, and scholarly literature, the study highlights how criminal liability serves as a potent deterrent against deliberate deceptive practices that cause substantial harm. Special focus is given to emerging challenges in the digital era, such as cross-border advertising crimes, artificial intelligence-driven ad targeting, and regulatory gaps that traditional frameworks struggle to address. Comparative analysis with international best practices underscores the need for more cohesive and adaptive regulatory models. Through case studies and doctrinal analysis, the research finds that stricter enforcement, technological integration, and increased public awareness are essential to counter the growing sophistication of false advertising strategies. The findings suggest a multi-dimensional approach combining legal reforms, regulatory vigilance, and global collaboration is critical for safeguarding consumer interests and ensuring fair market practices. This study provides valuable insights for policymakers, regulators, and legal practitioners committed to enhancing consumer protection in the evolving advertising landscape.

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 Keywords

False Advertising, Consumer Protection, Criminal Liability, Regulatory Frameworks, Digital Advertising, Cross-border Advertising, Artificial Intelligence, Market Integrity, Judicial Response, Advertising Law

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 Abstract

Swami Vivekanad ke shaikshik vicharo ki bharatiya gyan parampara ke sandarbh mein prasangikta

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Swami Vivekanad ke shaikshik vicharo ki bharatiya gyan parampara ke sandarbh mein prasangikta

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The emergence of omnichannel marketing has significantly transformed the interaction between businesses and consumers by offering a cohesive and integrated customer experience across various digital and physical platforms. This study investigates the extent of omnichannel marketing adoption among businesses in Indore, a rapidly developing tier-2 city in central India, and its impact on consumer behaviour and business engagement. By employing correlation and multiple regression analysis on data collected from a sample of 150 respondents, the research examines the relationship between omnichannel strategies and key consumer metrics, including satisfaction, loyalty, personalization, and frequency of interaction. The findings reveal a statistically significant and positive correlation between omnichannel marketing and consumer satisfaction. Moreover, the regression results confirm that omnichannel adoption substantially enhances customer engagement, with satisfaction, loyalty, and frequency of interaction emerging as strong predictors. These insights underscore the strategic importance of omnichannel approaches for businesses seeking to build long-term consumer relationships and remain competitive in evolving urban markets.

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 Keywords

Omnichannel Marketing, Consumer Satisfaction, Customer Engagement, Business Strategy, Loyalty, Personalization, Digital Marketing, Indore, Tier-2 Cities, Regression Analysis

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Guru Ravidas Ji, on one hand, is a spiritual heritage of the Indian tradition, and on the other hand, he is a treasure in terms of intellectual thought and reflection. In my article, I have attempted to evaluate "Guru Ravidas Ji's contribution to Indian culture and society." In this context, I have tried to understand his spiritual legacy, his socially resistant discourses, and his critical views on the caste system. Along with this, I have also tried to mark his place in the cultural movement and define his position within the Bhakti movement.

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 Keywords

Caste-System, Bhagati Movement, Spiritual discourse, Social discourse

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The increasing rate and complexity of cyberattacks in the financial industry have required the implementation of more robust cybersecurity technologies. Traditional threat detection technologies are not adequate in managing the dynamic nature of such attacks, hence the need for more advanced systems. This study investigates the convergence of Observability-Driven Cybersecurity and Artificial Intelligence (AI) technologies and the use of technologies like AppDynamics towards advanced threat detection in financial IT systems. Despite the growing adoption of AI and observability technologies, there is a broad research gap on how such technologies can be synergistically combined to offer proactive security solutions in real-time, particularly in the high-risk and complex environment of financial institutions. Current systems are ineffective in detecting advanced persistent threats (APTs), insider threats, and in formulating attack plans until damage has been caused. This study seeks to bridge the gap by investigating how observability tools, like AppDynamics, can be combined with AI algorithms to enable early detection and prevention of cybersecurity threats. The study investigates real-time anomaly detection, predictive threat intelligence, and automated response features to boost security operations and response times. Through the utilization of performance monitoring, behavioral analytics, and machine learning technologies, this study proposes a holistic solution to boost the cybersecurity defenses. This study adds to the growing literature on the convergence of AI technologies and observability, offering practical suggestions for financial institutions seeking to upgrade their cybersecurity systems against increasingly sophisticated attacks.

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 Keywords

Observability-based security, artificial intelligence-based financial systems, AppDynamics, threat detection, real-time anomaly detection, predictive threat intelligence, machine learning, financial IT security, insider threats, advanced persistent threats, automated response systems, cybersecurity frameworks.

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The application of Robot Framework for Acceptance Test Driven Development (ATDD) in functional testing of enterprise applications represents a transformative approach to quality assurance and software development. By integrating automated testing with ATDD practices, development teams can foster early collaboration among stakeholders, ensuring that functional requirements are clearly understood and met from the inception of a project. This methodology leverages the versatility and extensibility of the Robot Framework to design and execute comprehensive test suites that simulate real-world user scenarios in enterprise environments. The framework's keyword-driven approach simplifies test case creation, promotes reusability, and enhances the maintainability of test scripts, even in complex systems. Moreover, the adoption of ATDD facilitates continuous feedback, allowing developers to rapidly identify and resolve discrepancies between expected and actual system behaviors. This results in reduced defect rates and improved overall software quality. The integration of these practices also supports agile development cycles by aligning testing activities with iterative delivery and continuous integration pipelines. Consequently, organizations can achieve faster time-to-market while maintaining high standards of functionality and reliability. This study explores the benefits, challenges, and best practices associated with implementing Robot Framework for ATDD, providing insights into effective strategies for optimizing test automation in enterprise settings. It further examines case studies that demonstrate measurable improvements in efficiency and quality assurance outcomes, underlining the framework's capacity to meet the evolving demands of modern software development. Ultimately, the adoption of Robot Framework for ATDD not only broadens test coverage but also fosters continuous improvement and sustainable software quality.

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Robot Framework, ATDD, Functional Testing, Enterprise Applications, Test Automation, Quality Assurance, Agile Development, Continuous Integration, Software Testing, Automation Tools

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The rapid expansion of digital ecosystems has placed Application Programming Interfaces (APIs) at the core of modern connectivity, driving both innovation and security challenges. This abstract explores the future of API security by investigating emerging trends, advanced technologies, and adaptive strategies that are essential for safeguarding digital interactions. As organizations increasingly rely on cloud-based and microservices architectures, the complexity and number of attack vectors have grown exponentially. Traditional security measures are proving insufficient in the face of sophisticated cyber threats. In response, the integration of artificial intelligence and machine learning is revolutionizing threat detection by enabling real-time analysis and automated responses to potential vulnerabilities. Moreover, the adoption of zero-trust security models and adaptive authentication mechanisms is reshaping how sensitive data is protected, ensuring that every access request is rigorously verified. The evolving regulatory landscape further reinforces the need for proactive risk management and compliance-driven security strategies. Through an evaluation of contemporary case studies and industry best practices, this work outlines a multi-layered approach to API security that emphasizes continuous monitoring, periodic vulnerability assessments, and the implementation of robust encryption techniques. Ultimately, the convergence of innovative technologies and strategic foresight is setting the stage for a resilient API security framework. This paper offers actionable insights for security professionals, developers, and policymakers committed to enhancing the integrity and reliability of digital infrastructures in an increasingly interconnected world.

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 Keywords

API Security, Future Trends, Emerging Technologies, Machine Learning, Zero-Trust, Adaptive Authentication, Regulatory Compliance

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Real-Time Data Streaming with Spark and Kafka represents a pivotal approach in modern data analytics, where instantaneous processing and rapid insights are crucial. This methodology leverages Apache Spark's distributed computing capabilities and Apache Kafka's robust messaging framework to seamlessly ingest, process, and analyze voluminous data streams in real time. The integration of these technologies facilitates the handling of complex data pipelines, enabling organizations to derive actionable insights from continuous data flows generated by various sources such as social media, sensors, and transactional systems. Spark's in-memory computing accelerates data processing, while Kafka provides high-throughput, fault-tolerant messaging that ensures data reliability and scalability. Together, they address challenges such as latency, data consistency, and system resilience. The system architecture supports dynamic scaling and can accommodate fluctuating workloads, making it adaptable to different operational requirements. Additionally, real-time processing enables proactive decision-making by promptly identifying trends and anomalies, which is essential in sectors like finance, healthcare, and telecommunications. The paradigm also encourages the use of stream processing frameworks and machine learning models, enhancing predictive analytics and automated responses. Overall, the synergy between Spark and Kafka not only improves performance but also offers a cost-effective and flexible solution for real-time data analytics, paving the way for future innovations in big data technology and distributed systems. This abstract encapsulates the significance, operational mechanics, and transformative potential of real-time data streaming in today's digital landscape.

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Real-Time Data Streaming; Apache Spark; Apache Kafka; Big Data Analytics; In-Memory Computing; Distributed Systems; Data Pipelines

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This study explores the integration of artificial intelligence and machine learning into large-scale Oracle Cloud ERP deployments to revolutionize enterprise operations. By incorporating advanced analytics, predictive modeling, and process automation, these technologies transform conventional ERP systems into dynamic platforms that enhance efficiency and decision-making capabilities. The infusion of AI enables real-time data processing and sophisticated insights, which lead to improved resource allocation, optimized workflows, and proactive risk management. Machine learning algorithms, through continuous learning from historical and real-time data, empower the system to adapt to evolving business needs and forecast trends with heightened accuracy. This integration not only minimizes manual intervention but also enhances data quality and operational scalability across complex organizational structures. Case studies illustrate substantial cost reductions, heightened productivity, and strategic agility in environments where Oracle Cloud ERP systems are bolstered by AI-driven solutions. The paper also addresses implementation challenges, including data integration issues, cybersecurity risks, and the need for robust change management strategies to support technological advancements. In conclusion, the research underscores the transformative potential of AI and machine learning in modernizing ERP systems, paving the way for more resilient, efficient, and competitive business operations in an increasingly digital marketplace. This study provides a framework for organizations looking to harness these technologies, offering insights into both the benefits and the practical considerations essential for successful deployment.

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 Keywords

Oracle Cloud ERP, AI integration, machine learning, digital transformation, process automation, predictive analytics, enterprise resource planning

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Creative Commons Attribution 4.0 and The Open Definition

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Generative artificial intelligence (AI) is revolutionizing how businesses leverage vast data lakes to support critical decision-making processes. Data lakes, characterized by their ability to store diverse and large-scale datasets, pose significant challenges in extracting actionable insights efficiently. Generative AI, encompassing technologies such as generative adversarial networks (GANs) and large language models (LLMs), addresses these challenges by automating complex data management tasks, enhancing data quality, and generating predictive insights. This literature review investigates recent advancements and applications of generative AI within large-scale data lakes, highlighting its transformative impact on business analytics, risk management, and operational optimization. Through synthesizing findings from current research, the review explores practical implementations, evaluating benefits such as accelerated insight generation, improved scalability, and enhanced interpretability of complex datasets. Moreover, this paper discusses critical considerations including data governance, ethical implications of AI-generated insights, and integration challenges within existing enterprise systems. Emphasis is placed on how generative AI facilitates more responsive and agile decision-making frameworks, ultimately enhancing competitive advantage and strategic responsiveness in dynamic market conditions. The synthesis aims to provide a comprehensive understanding for business leaders, data scientists, and IT professionals on effectively deploying generative AI capabilities to maximize value extraction from extensive corporate data resources.

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 Keywords

Generative AI, data lakes, business analytics, large-scale datasets, decision-making, GANs, predictive insights

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